A New Variant Particle Swarm Optimization

基于对现实中鸟的飞行方式的模拟,提出了一种新的变异粒子群优化算法(VPSO).该算法增加了粒子的飞行(搜索)模式,粒子具有随时调整其飞行(搜索)方式的能力.实验结果表明:笔者算法在一定程度上改善了标准PSO存在的易陷入局部最优之不足,具有比标准PSO更强的跳出局部最优的能力和更好的全局优化能力,可用于求解高维复杂优化问题.In this paper, a new optimization approach called variant particle swarm optimization （VP- SO） is proposed based on simulating the real bird＇s flight modes. In the VPSO, every particle can use more than one flight （search） mode flying while it is searching for food, has the ability of adjusting its flight （search） modes at any time. In order to test the performance of the VPSO, numerical experiments were done on some typical high-dimensional and complex optimization problems, such as Schwefel＇s function, Rastrigin＇s function, Step function, Schwefel＇s function, Sphere function, Griewank＇s function, Rotated hyper-ellip- soid function, and Zakharov＇s function. The numerical experimental results indicate that the VPSO has, to a certain extent, improved the shortcoming of easy being fallen into local optimum which exists in the normal PSO, and has stronger ability to jump out of local optimum and better ability of global optimization than the normal PSO. The VPSO can be used to solve the complex and the high-dimensional ontimization orohlerns.2015年度广西高等学校科学技术研究项目(KY2015YB078

中国植物应答环境变化研究的过去与未来

中华人民共和国建国70周年,特别是改革开放40年以来,中国科技工作者在植物研究领域取得了举世瞩目的成绩.这篇综述简要地总结了中国植物学家以模式植物拟南芥,以及水稻、玉米、小麦和棉花等农作物为研究材料,在植物应答非生物逆境胁迫,包括干旱、高温、低温、盐碱、重金属、铝毒害和光胁迫等领域的基础研究和应用成果;同时也提出了植物非生物逆境研究领域亟待解决的重大问题、作物稳产分子设计的重大需求和创制耐受多种逆境环境的绿色新种质的可能性